def cost(self, readouts, outputs):
mu, sigma, coeff = self.components(readouts)
#ipdb.set_trace()
return GMM(outputs, mu, sigma, coeff)
bricks = [mlp_x, transition, mlp_gmm]
for brick in bricks:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.)
brick.initialize()
##############
# Test model
##############
x_g = mlp_x.apply(x)
h = transition.apply(x_g)
mu, sigma, coeff = mlp_gmm.apply(h[-2])
cost = GMM(y, mu, sigma, coeff)
cost = cost.mean()
cost.name = 'sequence_log_likelihood'
cg = ComputationGraph(cost)
model = Model(cost)
#################
# Algorithm
#################
n_batches = 139 * 16
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule(